Behlee Aimone

Contact:

Phone: 307-780-5293

About:

Hi, I’m Behlee; dog mom, lover of treats, bacteria, the office, JB, and the color pink! I grew up, the youngest of eight, on a family ranch in rural Wyoming (like seriously… RURAL) where I worked cattle, participated in sports and did normal kid things. I moved to the big city (Orem Utah) in 2018 right after I graduated high school to pursue my academic dreams at Utah Valley University. I found the love of my life in calculus and got married in August of 2019 (very Utah… I know) and continued to fall in love with Biology and all things science. During my time at UVU, I have been honored and privileged to work in two undergraduate research labs with Dr. Lauren Brooks and Dr. Geoff Zahn. In both Dr. Brooks’s and Dr. Zahn’s labs I led funded research teams of undergraduates and presented our findings at local poster sessions. Peer mentoring and team-based learning have become passions of mine at UVU and have taught me that inclusivity and accessibility are the foundation of collaboration. I am excited to continue collaborating and working with others as I enter the workforce.
I became passionate about data science after working with Dr. Zahn in his intro to r studio class. Since taking his class I have created a shiny app currently in use in the research labs at UVU and have actively participated in preparing a manuscript for publication on the microbiome of seagrass in South Asia. I experienced how fulfilling it is to work with complex data structures, use machine learning, scrape web data, do complex statistical analysis (MRM, NMDS, decision trees, etc.), and work on code collaboratively. Data analytics excites me. I love that it is an ever-changing field that is fast-paced and challenging. I am ready to continue improving my coding skills and learn innovative ways to utilize data because the future should be data-driven, and data tells stories if we have to tools to see them.

Personal links/code contributions:

Git hub

Zenodo release

Research Experience:

SAC funded research on slime molds ability to consume and control pathogenic bacterial growth

Nucleotide analysis on the phylogenetic relationships of poison dart frogs in Central and South America

analysis performed using NCBI, NGPhylogeny, and FigTree v1.4.4

Sculpt funded research of biocide efficacy on S. aurues

analysis performed using R studio

Output/Code examples:

Here is a glimpse of a real-life data set about covid that I scraped and cleaned from the CDC
province_state last_update confirmed deaths recovered active case_fatality_ratio
Alabama 2021-01-02 365747 4872 202137 158738 1.3320683
Alaska 2021-01-02 47019 206 7165 39648 0.4381208
Arizona 2021-01-02 530267 9015 76934 444318 1.7000869
Arkansas 2021-01-02 229442 3711 199247 26484 1.6174022
California 2021-01-02 2436449 26168 NA NA 1.0736043
Colorado 2021-01-02 337161 4873 18102 314186 1.4453036
We can rank states by their maximum fatality ratio
And show the cumulative death toll over time (this plot is interactive if you scroll your cursor over the line)
Analysis of a data set regarding faculty salaries
## # A tibble: 6 × 17
##   FedID UnivName     State Tier  AvgFu…¹ AvgAs…² AvgAs…³ AvgPr…⁴ AvgFu…⁵ AvgAs…⁶
##   <dbl> <chr>        <chr> <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1  1061 Alaska Paci… AK    IIB       454     382     362     382     567     485
## 2  1063 Univ.Alaska… AK    I         686     560     432     508     914     753
## 3  1065 Univ.Alaska… AK    IIA       533     494     329     415     716     663
## 4 11462 Univ.Alaska… AK    IIA       612     507     414     498     825     681
## 5  1002 Alabama Agr… AL    IIA       442     369     310     350     530     444
## 6  1004 University … AL    IIA       441     385     310     388     542     473
## # … with 7 more variables: AvgAssistProfComp <dbl>, AvgProfCompAll <dbl>,
## #   NumFullProfs <dbl>, NumAssocProfs <dbl>, NumAssistProfs <dbl>,
## #   NumInstructors <dbl>, NumFacultyAll <dbl>, and abbreviated variable names
## #   ¹​AvgFullProfSalary, ²​AvgAssocProfSalary, ³​AvgAssistProfSalary,
## #   ⁴​AvgProfSalaryAll, ⁵​AvgFullProfComp, ⁶​AvgAssocProfComp
This is not a “tidy” data set, so I can clean it by writing a function that can be used over and over
fed_id univ_name state tier avg_prof_salary_all avg_prof_comp_all num_instructors num_faculty_all rank salary comp_type comp_amt faculty_type faculty_count
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_full_prof_comp 567 num_full_profs 6
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_full_prof_comp 567 num_assoc_profs 11
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_full_prof_comp 567 num_assist_profs 9
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_assoc_prof_comp 485 num_full_profs 6
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_assoc_prof_comp 485 num_assoc_profs 11
1061 Alaska Pacific University AK IIB 382 487 4 32 full_prof_salary 454 avg_assoc_prof_comp 485 num_assist_profs 9
Real-life data sets can be pretty hard to understand. It is my job to make them manageable to look at and informative!

The most powerful tool a data analyst has (besides visualizing data), is modeling data to make predictions.
##                Df    Sum Sq  Mean Sq F value Pr(>F)    
## state          51  51141561  1002776   280.1 <2e-16 ***
## rank            2 151020520 75510260 21089.7 <2e-16 ***
## tier            2  61398662 30699331  8574.2 <2e-16 ***
## Residuals   30139 107910649     3580                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1152 observations deleted due to missingness
This is an ANOVA model which is a linear modeling method to evaluate the relationships between variables. It can rank the variables based on their impact on the outcome. We can use tools like this to identify variables to explore in making changes to our experiments, and workflow or to make predictions for the future.
ANOVA is just one method of modeling. There are countless others that are readily usable with R studio. It is my job to use the objectivity of the data software to select the model that best fits each unique data set.